 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O43847 from www.uniprot.org...
The NucPred score for your sequence is 0.49 (see score help below)
1 MLRRVTVAAVCATRRKLCEAGRELAALWGIETRGRCEDSAAARPFPILAM 50
51 PGRNKAKSTCSCPDLQPNGQDLGENSRVARLGADESEEEGRRGSLSNAGD 100
101 PEIVKSPSDPKQYRYIKLQNGLQALLISDLSNMEGKTGNTTDDEEEEEVE 150
151 EEEEDDDEDSGAEIEDDDEEGFDDEDEFDDEHDDDLDTEDNELEELEERA 200
201 EARKKTTEKQSAAALCVGVGSFADPDDLPGLAHFLEHMVFMGSLKYPDEN 250
251 GFDAFLKKHGGSDNASTDCERTVFQFDVQRKYFKEALDRWAQFFIHPLMI 300
301 RDAIDREVEAVDSEYQLARPSDANRKEMLFGSLARPGHPMGKFFWGNAET 350
351 LKHEPRKNNIDTHARLREFWMRYYSSHYMTLVVQSKETLDTLEKWVTEIF 400
401 SQIPNNGLPRPNFGHLTDPFDTPAFNKLYRVVPIRKIHALTITWALPPQQ 450
451 QHYRVKPLHYISWLVGHEGKGSILSFLRKKCWALALFGGNGETGFEQNST 500
501 YSVFSISITLTDEGYEHFYEVAYTVFQYLKMLQKLGPEKRIFEEIRKIED 550
551 NEFHYQEQTDPVEYVENMCENMQLYPLQDILTGDQLLFEYKPEVIGEALN 600
601 QLVPQKANLVLLSGANEGKCDLKEKWFGTQYSIEDIENSWAELWNSNFEL 650
651 NPDLHLPAENKYIATDFTLKAFDCPETEYPVKIVNTPQGCLWYKKDNKFK 700
701 IPKAYIRFHLISPLIQKSAANVVLFDIFVNILTHNLAEPAYEADVAQLEY 750
751 KLVAGEHGLIIRVKGFNHKLPLLFQLIIDYLAEFNSTPAVFTMITEQLKK 800
801 TYFNILIKPETLAKDVRLLILEYARWSMIDKYQALMDGLSLESLLSFVKE 850
851 FKSQLFVEGLVQGNVTSTESMDFLKYVVDKLNFKPLEQEMPVQFQVVELP 900
901 SGHHLCKVKALNKGDANSEVTVYYQSGTRSLREYTLMELLVMHMEEPCFD 950
951 FLRTKQTLGYHVYPTCRNTSGILGFSVTVGTQATKYNSEVVDKKIEEFLS 1000
1001 SFEEKIENLTEEAFNTQVTALIKLKECEDTHLGEEVDRNWNEVVTQQYLF 1050
1051 DRLAHEIEALKSFSKSDLVNWFKAHRGPGSKMLSVHVVGYGKYELEEDGT 1100
1101 PSSEDSNSSCEVMQLTYLPTSPLLADCIIPITDIRAFTTTLNLLPYHKIV 1150
1151 K 1151
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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